Linear Algebra in Machine Learning

Every neural network — from a spam filter to a large language model — is, at its core, a towering stack of matrix multiplications. Every recommendation, every image caption, every translation is a vector journey through learned transformations. PCA compresses data with eigenvectors; embeddings turn words into directions in high-dimensional space. If you understand linear algebra, you understand the machinery of modern AI.